AI-Powered Scheduling

Field service scheduling is used to dispatch employed and contracted field workers for service jobs. Scheduling workers manually is extremely time consuming and virtually impossible to perform efficiently, resulting in wasted dispatcher time, poor management of field workers daily schedules, lost productivity, and ultimately unhappy customers.

Software that schedules field workers helps eliminate some of the errors that can occur with manual scheduling. Field service scheduling attempts to consistently and efficiently meet changing needs and changing demands to ensure customers are serviced as quickly and efficiently as possible.

Schedule optimization improves on basic field service scheduling by accounting for more variables, parameters, and business constraints to deliver finely tuned schedules that bring a higher level of satisfaction to the end customer, make the lives of dispatchers less stressful, enhance the field worker experience, and make companies more profitable by lowering costs and increasing revenue.

Schedule optimization considers complex factors such as skills, location, and equipment availability that can influence the success of workforce scheduling. These factors are all considered when determining and optimizing schedules, along with the type and complexity of the work, and finding a balance among all these factors, parameter, and the workflows and processes. Adhering to all these and finding the balance yields an optimized schedule that provides an organization with efficiencies throughout the field.

Schedule optimization improves worker productivity, reduces costs, improves operational efficiency and profitability, employee satisfaction and retention, and helps organizations deliver exceptional customer experiences.

No. Route optimization is focused on determining the most cost-efficient route. It can include variables such as driver schedules, available hours, total stops, fulfillment estimates, and legal requirements, so it is not simply about finding the shortest path from point A to point B. But it does not include numerous other factors and parameters that are included in schedule optimization, include field worker skills, tasks, availability, capacity, prioritization, activity coordination and time slots to maximize workforce efficiency.

No. The ultimate goal of schedule optimization is to ensure the best result for the customer while simultaneously maximizing the field service organization's operational efficiency. This means ensuring the following for the consumer:

• the best field worker services the job
• the field service worker is armed with the right parts and information for all jobs
• the field worker selected costs the least and is the most profitable given all constraints
• the field service worker is dispatched within the least amount of time possible

Traditional schedule optimization attempts to reach these goals but typically has limited tools to accomplish them.

For example, finding the best field worker may result in dispatching an acceptable one -- one that services a particular brand and has a certain number of years' experience. But it does not take into account unforeseen major weather or traffic events or other technicians’ schedules.

Furthermore, when taking into account blended workforces – those that include employees as well as contractors – the challenge of delivering the optimal field resource, which may be an employee or may be a contractor, is usually too much to ask of traditional field service management systems.

To really find the best field resource and also support them with the exact parts and information needed to service the job properly, at minimal acceptable cost without sacrificing service quality, and dispatch the job quickly, a field service management system needs to account for a plethora of variables. Only by accommodating an organization’s wide range of unique requirements can a system truly optimize the schedule.

The system should consider unmovable or unchangeable constraints. For example, limitations such as an employee’s work schedule or a contractor’s stated availability cannot change -- and the schedule optimization tool must be aware of that. If a technician needs to work in a facility that is not open or does not allow access during certain hours, the system must be mindful of that.

Similarly, organizations typically have their own set of variable constraints based on their unique business or other requirements. This can include travel policies and costs, preferred customers, overtime policies, KPIs (e.g. efficiency, training, profitability), and more – the list is long, but critical to achieving an organization’s goals and priorities.

Importantly, conditions can and do shift throughout the day. If a schedule “optimization” system does not adjust accordingly, the optimization fails.

Only by considering all these factors can a truly optimizes schedule be created.